Talk by Prof. Nathan Srebro (Toyota Technological Institute Chicago, USA)

Prof. Nathan Srebro (Toyota Technological Institute Chicago, USA)
http://ttic.uchicago.edu/~nati/

Titile:
Supervised Learning without Discrimination

Abstract:
As machine learning is increasingly being used in areas protected by
anti discrimination law, or in other domains which are socially and
morally sensitive, the problem of algorithmicly measuring and avoiding
prohibited discrimination in machine learning is pressing. What does
it mean for a predictor to not discriminate with respect to protected
group (e.g. according to race, gender, etc)? We propose a notion of
non-discrimination that can be measured statistically, used
algorithmicly, and avoids many of the pitfalls of previous
definitions. We further study what type of discrimination and
non-discrimination can be identified with oblivious tests, which treat
the predictor as an opaque black-box, and what different oblivious
tests tell us about possible discrimination.

Joint work with Suriya Gunasekar, Mortiz Hardt, Mesrob Ohannessian,
Eric Pierce and Blake Woodwoorth